Prepare data
Read and format data
Prevalence
df_uk_prev <- read_csv('UK_timeseries_prep_2005.csv')
Parsed with column specification:
cols(
ut_area = [31mcol_character()[39m,
date = [31mcol_character()[39m,
cumcase = [32mcol_double()[39m,
poptotal = [32mcol_double()[39m,
rate = [32mcol_double()[39m
)
df_uk_prev <- df_uk_prev %>%
select(ut_area, date, rate) %>%
rename(rate_day = rate) %>%
mutate(date = as.Date(date, "%d%b%Y"))
df_uk_prev2 <- read_csv('UK_timeseries_prep_0506_laua_all.csv')
Parsed with column specification:
cols(
.default = col_double(),
laua_name = [31mcol_character()[39m,
laua = [31mcol_character()[39m,
date = [31mcol_character()[39m
)
See spec(...) for full column specifications.
df_uk_prev2 <- df_uk_prev2 %>%
select(laua, date, males, popdens, manufacturing, tourism, health,
academic, medage, conservative, airport_dist, medinc, open,
extra, agree, neuro, sci, rate) %>%
rename(pers_o = open,
pers_c = sci,
pers_e = extra,
pers_a = agree,
pers_n = neuro,
rate_day = rate) %>%
mutate(date = as.Date(date, "%d%b%Y"))
df_uk_prev2 %>% select(-date, -rate_day) %>%
distinct() %>% write_csv('df_uk_pers_laua.csv')
Personality
df_uk_pers <- read_csv('timeseries_uk_utla_march9_april_09.csv')
Parsed with column specification:
cols(
ut_area = [31mcol_character()[39m,
time = [32mcol_double()[39m,
areaname = [31mcol_character()[39m,
open = [32mcol_double()[39m,
extra = [32mcol_double()[39m,
agree = [32mcol_double()[39m,
neuro = [32mcol_double()[39m,
sci = [32mcol_double()[39m,
frequ = [32mcol_double()[39m,
ut_name = [31mcol_character()[39m,
poptotal = [32mcol_double()[39m,
rate_day = [32mcol_double()[39m
)
df_uk_pers <- df_uk_pers %>%
select(ut_area, open, agree, neuro, sci, extra) %>%
dplyr::rename(pers_o = open,
pers_c = sci,
pers_e = extra,
pers_a = agree,
pers_n = neuro) %>%
distinct()
df_uk_pers %>% write_csv('df_uk_pers_ut.csv')
df_uk_pers_nuts <- read_csv('UK_socdist_fb_nuts3.csv')
Parsed with column specification:
cols(
nuts3 = [31mcol_character()[39m,
date = [34mcol_date(format = "")[39m,
all_day_bing_tiles_visited_relat = [32mcol_double()[39m,
all_day_ratio_single_tile_users = [32mcol_double()[39m,
open = [32mcol_double()[39m,
extra = [32mcol_double()[39m,
agree = [32mcol_double()[39m,
neuro = [32mcol_double()[39m,
sci = [32mcol_double()[39m,
frequ = [32mcol_double()[39m,
nuts3_name = [31mcol_character()[39m,
runday = [32mcol_double()[39m
)
df_uk_pers_nuts <- df_uk_pers_nuts %>%
select(nuts3, open, sci, extra, agree, neuro) %>%
dplyr::rename(pers_o = open,
pers_c = sci,
pers_e = extra,
pers_a = agree,
pers_n = neuro) %>%
distinct()
df_uk_pers_nuts
NA
Social distancing
fb_files <- list.files('../FB Data/UK individual files',
'*.csv', full.names = T)
df_uk_socdist <- fb_files %>%
map(read.csv) %>% bind_rows()
df_uk_socdist <- df_uk_socdist %>%
select(ds, all_day_bing_tiles_visited_relative_change,
all_day_ratio_single_tile_users, external_polygon_id) %>%
rename(date = ds,
nuts3 = external_polygon_id,
socdist_tiles = all_day_bing_tiles_visited_relative_change,
socdist_single_tile = all_day_ratio_single_tile_users) %>%
mutate(nuts3 = as.character(nuts3),
date = as.Date(date)) %>%
arrange(nuts3, date) %>%
drop_na()
df_uk_socdist %>% select(nuts3) %>% distinct() %>% nrow()
Controls
df_uk_ctrl_nuts <- read_csv("controls_UK_nuts3.csv")
Parsed with column specification:
cols(
nuts3 = [31mcol_character()[39m,
nuts3_name = [31mcol_character()[39m,
airport_dist = [32mcol_double()[39m,
males = [32mcol_double()[39m,
popdens = [32mcol_double()[39m,
manufacturing = [32mcol_double()[39m,
tourism = [32mcol_double()[39m,
health = [32mcol_double()[39m,
academic = [32mcol_double()[39m,
medinc = [32mcol_double()[39m,
medage = [32mcol_double()[39m,
conservative = [32mcol_double()[39m
)
df_uk_ctrl_nuts <- df_uk_ctrl_nuts %>% select(-nuts3_name)
df_uk_ctrl_nuts
df_uk_ctrl_ut <- read_csv("controls_UK_ut.csv")
Parsed with column specification:
cols(
ut_area = [31mcol_character()[39m,
ut_name = [31mcol_character()[39m,
airport_dist = [32mcol_double()[39m,
males = [32mcol_double()[39m,
popdens = [32mcol_double()[39m,
manufacturing = [32mcol_double()[39m,
tourism = [32mcol_double()[39m,
health = [32mcol_double()[39m,
academic = [32mcol_double()[39m,
medinc = [32mcol_double()[39m,
medage = [32mcol_double()[39m,
conservative = [32mcol_double()[39m
)
df_uk_ctrl_ut <- df_uk_ctrl_ut %>% select(-ut_name)
df_uk_ctrl_ut
NA
NA
Merge prevalence data
# create sequence of dates
date_sequence <- seq.Date(min(df_uk_prev2$date),
max(df_uk_prev2$date), 1)
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence))
names(df_dates) <- c('date', 'time')
# merge day index with gps data
df_uk_prev2 = df_uk_prev2 %>%
merge(df_dates, by='date') %>%
arrange(laua) %>%
as_tibble()
df_uk_prev2
NA
Merge social distancing data
Check timeframes
df_uk_prev$date %>% summary()
df_uk_socdist$date %>% summary()
Control for weekend effect in social distancing
easter <- seq.Date(as.Date('2020-04-10'), as.Date('2020-04-13'), 1)
df_uk_loess <- df_uk_socdist %>%
mutate(weekday = format(date, '%u')) %>%
filter(!(weekday %in% c('6','7') | date %in% easter)) %>%
split(.$nuts3) %>%
map(~ loess(socdist_single_tile ~ time, data = .)) %>%
map(predict, 1:max(df_uk_socdist$time)) %>%
bind_rows() %>%
gather(key = 'nuts3', value = 'loess') %>%
group_by(nuts3) %>%
mutate(time = row_number())
df_uk_loess_2 <- df_uk_socdist %>%
mutate(weekday = format(date, '%u')) %>%
filter(!(weekday %in% c('6','7') | date %in% easter)) %>%
split(.$nuts3) %>%
map(~ loess(socdist_tiles ~ time, data = .)) %>%
map(predict, 1:max(df_uk_socdist$time)) %>%
bind_rows() %>%
gather(key = 'nuts3', value = 'loess') %>%
rename(loess_2 = loess) %>%
group_by(nuts3) %>%
mutate(time = row_number())
df_uk_socdist <- df_uk_socdist %>%
merge(df_uk_loess, by=c('nuts3', 'time')) %>%
merge(df_uk_loess_2, by=c('nuts3', 'time')) %>%
mutate(weekday = format(date, '%u')) %>%
mutate(socdist_single_tile_clean = ifelse(weekday %in% c('6','7') | date %in% easter,
loess, socdist_single_tile),
socdist_tiles_clean = ifelse(weekday %in% c('6','7') | date %in% easter,
loess_2, socdist_tiles)) %>%
arrange(nuts3, time) %>%
select(-weekday)
df_uk_socdist <- df_uk_socdist %>% drop_na() %>% mutate(time = time-1)
Explore data
Plot prevalence over time
df_uk_prev %>% ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=ut_area, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_uk_prev %>% mutate(prev_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(prev_tail != 'center') %>%
ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=ut_area, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~prev_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





df_uk_prev2 %>% ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=laua, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_uk_prev2 %>% mutate(prev_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(prev_tail != 'center') %>%
ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=laua, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~prev_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





Plot social distancing over time
df_uk_socdist %>% ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=nuts3, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall social distancing over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_uk_socdist %>% mutate(socdist_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(socdist_tail != 'center') %>%
ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=nuts3, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~socdist_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





df_uk_socdist %>% ggplot(aes(x=time, y=socdist_tiles)) +
geom_point(aes(col=nuts3, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall social distancing over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_uk_socdist %>% mutate(socdist_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(socdist_tail != 'center') %>%
ggplot(aes(x=time, y=socdist_tiles)) +
geom_point(aes(col=nuts3, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~socdist_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





df_uk_socdist <- df_uk_socdist %>%
mutate(socdist_single_tile = socdist_single_tile_clean,
socdist_tiles = socdist_tiles_clean) %>%
select(-loess, -loess_2, -socdist_single_tile_clean, -socdist_tiles_clean)
Correlations
df_uk_prev %>% group_by(ut_area) %>%
summarize_if(is.numeric, mean, na.rm=T) %>%
select(-ut_area, -time) %>%
cor(use = 'pairwise.complete') %>% round(3) %>%
as.data.frame()
df_uk_socdist %>% group_by(nuts3) %>%
summarize_if(is.numeric, mean, na.rm=T) %>%
select(-nuts3, -time) %>%
cor(use = 'pairwise.complete') %>% round(3) %>%
as.data.frame()
NA
Rescale Data
lvl2_scaled_ut <- df_uk_prev %>%
dplyr::select(-time, -date, -rate_day) %>%
distinct() %>%
mutate_at(vars(-ut_area), scale)
lvl1_scaled_ut <- df_uk_prev %>% select(ut_area, time, rate_day)
df_uk_prev_scaled <- plyr::join(lvl1_scaled_ut, lvl2_scaled_ut, by = 'ut_area')
df_uk_prev_scaled
lvl2_scaled_laua <- df_uk_prev2 %>%
dplyr::select(-time, -date, -rate_day) %>%
distinct() %>%
mutate_at(vars(-laua), scale)
lvl1_scaled_laua <- df_uk_prev2 %>% select(laua, time, rate_day)
df_uk_prev2_scaled <- plyr::join(lvl1_scaled_laua, lvl2_scaled_laua, by = 'laua')
df_uk_prev2_scaled
NA
lvl2_scaled_nuts <- df_uk_socdist %>%
dplyr::select(-time, -date, -socdist_tiles, -socdist_single_tile) %>%
distinct() %>%
mutate_at(vars(-nuts3), scale)
lvl1_scaled_nuts <- df_uk_socdist %>%
select(nuts3, time, socdist_single_tile, socdist_tiles) %>%
mutate_at(vars(-nuts3, -time), scale)
df_uk_socdist_scaled <- plyr::join(lvl1_scaled_nuts, lvl2_scaled_nuts, by = 'nuts3')
df_uk_socdist_scaled
NA
Predict Prevalence UT Level
Explore distributions
df_uk_onset_prev %>% ggplot(aes(onset_prev)) + geom_histogram()
df_uk_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram()
Predict COVID onset with time-to-event regression
# predict onset from personality
cox_onset_prev <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_onset_prev)
cox_onset_prev %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n, data = df_uk_onset_prev)
n= 149, number of events= 149
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.322264 1.380249 0.166864 1.931 0.0534 .
pers_c 0.036475 1.037149 0.135119 0.270 0.7872
pers_e -0.013895 0.986201 0.143396 -0.097 0.9228
pers_a 0.005323 1.005338 0.106927 0.050 0.9603
pers_n -0.092400 0.911740 0.106197 -0.870 0.3843
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.3802 0.7245 0.9952 1.914
pers_c 1.0371 0.9642 0.7958 1.352
pers_e 0.9862 1.0140 0.7446 1.306
pers_a 1.0053 0.9947 0.8153 1.240
pers_n 0.9117 1.0968 0.7404 1.123
Concordance= 0.605 (se = 0.027 )
Likelihood ratio test= 12.64 on 5 df, p=0.03
Wald test = 13.47 on 5 df, p=0.02
Score (logrank) test = 13.78 on 5 df, p=0.02
# predict onset from personality with controls
cox_onset_prev_ctrl <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative,
data = df_uk_onset_prev)
cox_onset_prev_ctrl %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n + airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage + conservative,
data = df_uk_onset_prev)
n= 144, number of events= 144
(5 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.13431 1.14375 0.26537 0.506 0.6128
pers_c 0.27571 1.31746 0.21410 1.288 0.1978
pers_e -0.28546 0.75167 0.16166 -1.766 0.0774 .
pers_a 0.15618 1.16904 0.12936 1.207 0.2273
pers_n -0.04289 0.95801 0.13821 -0.310 0.7563
airport_dist 0.08819 1.09219 0.10241 0.861 0.3892
males -0.28751 0.75013 0.11525 -2.495 0.0126 *
popdens 0.31764 1.37388 0.22294 1.425 0.1542
manufacturing -0.01434 0.98576 0.15053 -0.095 0.9241
tourism 0.20101 1.22263 0.12897 1.559 0.1191
health -0.13046 0.87769 0.09930 -1.314 0.1889
academic 0.41323 1.51170 0.25879 1.597 0.1103
medinc -0.10312 0.90201 0.15281 -0.675 0.4998
medage -0.54104 0.58214 0.21413 -2.527 0.0115 *
conservative -0.05175 0.94957 0.28447 -0.182 0.8557
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.1438 0.8743 0.6799 1.9241
pers_c 1.3175 0.7590 0.8660 2.0044
pers_e 0.7517 1.3304 0.5475 1.0319
pers_a 1.1690 0.8554 0.9072 1.5064
pers_n 0.9580 1.0438 0.7307 1.2561
airport_dist 1.0922 0.9156 0.8936 1.3350
males 0.7501 1.3331 0.5985 0.9402
popdens 1.3739 0.7279 0.8875 2.1267
manufacturing 0.9858 1.0144 0.7339 1.3240
tourism 1.2226 0.8179 0.9496 1.5742
health 0.8777 1.1394 0.7225 1.0663
academic 1.5117 0.6615 0.9103 2.5104
medinc 0.9020 1.1086 0.6686 1.2170
medage 0.5821 1.7178 0.3826 0.8857
conservative 0.9496 1.0531 0.5437 1.6583
Concordance= 0.643 (se = 0.027 )
Likelihood ratio test= 37.26 on 15 df, p=0.001
Wald test = 39.13 on 15 df, p=6e-04
Score (logrank) test = 40.98 on 15 df, p=3e-04
Predict prevalence slopes with linear models
# predict slopes from personality
lm_slope_prev <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_slope_prev)
lm_slope_prev %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_uk_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-3.7474 -0.3538 0.2157 0.5971 1.6747
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003326 0.081259 0.041 0.967406
pers_o -0.335161 0.160109 -2.093 0.038150 *
pers_c -0.471364 0.134224 -3.512 0.000602 ***
pers_e 0.045912 0.149655 0.307 0.759467
pers_a 0.087439 0.115589 0.756 0.450657
pers_n -0.172004 0.120288 -1.430 0.154997
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9747 on 138 degrees of freedom
Multiple R-squared: 0.08317, Adjusted R-squared: 0.04995
F-statistic: 2.504 on 5 and 138 DF, p-value: 0.03328
lm_slope_prev %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.1312354 0.13788821
pers_o -0.6002971 -0.07002450
pers_c -0.6936350 -0.24909386
pers_e -0.2019112 0.29373593
pers_a -0.1039721 0.27885073
pers_n -0.3711981 0.02719003
# predict slopes from personality with controls
lm_slope_prev_ctrl <- lm(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev,
data = df_uk_slope_prev)
lm_slope_prev_ctrl %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n + airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage + conservative +
onset_prev, data = df_uk_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-2.05366 -0.46070 0.00142 0.46146 1.97316
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.006093 0.062803 0.097 0.9229
pers_o -0.107115 0.189739 -0.565 0.5734
pers_c -0.314027 0.148365 -2.117 0.0362 *
pers_e -0.132175 0.124631 -1.061 0.2909
pers_a 0.244855 0.109204 2.242 0.0267 *
pers_n -0.172078 0.111285 -1.546 0.1245
airport_dist -0.127434 0.082936 -1.537 0.1269
males -0.220415 0.094095 -2.342 0.0207 *
popdens 0.313726 0.160336 1.957 0.0526 .
manufacturing 0.077796 0.107872 0.721 0.4721
tourism -0.016370 0.091325 -0.179 0.8580
health -0.033028 0.079300 -0.416 0.6778
academic 0.125265 0.192659 0.650 0.5167
medinc 0.146443 0.116369 1.258 0.2105
medage -0.059607 0.155676 -0.383 0.7024
conservative 0.134361 0.208331 0.645 0.5201
onset_prev 0.589208 0.069384 8.492 4.6e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7522 on 127 degrees of freedom
Multiple R-squared: 0.4975, Adjusted R-squared: 0.4342
F-statistic: 7.859 on 16 and 127 DF, p-value: 1.121e-12
lm_slope_prev_ctrl %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.09796849 0.110153915
pers_o -0.42150130 0.207271247
pers_c -0.55985809 -0.068195391
pers_e -0.33868197 0.074331811
pers_a 0.06391090 0.425799311
pers_n -0.35647153 0.012315179
airport_dist -0.26485443 0.009986441
males -0.37632416 -0.064506045
popdens 0.04805812 0.579393990
manufacturing -0.10094080 0.256532733
tourism -0.16768973 0.134950274
health -0.16442352 0.098367638
academic -0.19395953 0.444488789
medinc -0.04637384 0.339260214
medage -0.31755259 0.198338423
conservative -0.21083119 0.479552475
onset_prev 0.47424351 0.704172203
CRF predicting slopes
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_slope_prev <- cforest(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev,
data = df_uk_slope_prev,
controls = ctrls)
crf_slope_prev_varimp <- varimp(crf_slope_prev, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev, conditional = T, nperm = 1)
crf_slope_prev_varimp
pers_o pers_c pers_e pers_a pers_n airport_dist
0.0128740502 0.0185184287 -0.0018344699 -0.0034459553 -0.0047870867 0.0471837405
males popdens manufacturing tourism health academic
-0.0009166694 0.0159040281 -0.0020926242 0.0182207566 -0.0015815550 0.0023041324
medinc medage conservative onset_prev
0.0124853594 0.0028673495 0.0002912342 0.5122363597
crf_slope_prev_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond
pers_o pers_c pers_e pers_a pers_n airport_dist
0.013531981 0.015370914 0.001291142 -0.004426900 -0.002525750 0.053357454
males popdens manufacturing tourism health academic
0.002060008 0.018857165 -0.003769689 0.013425660 0.000157888 0.004189666
medinc medage conservative onset_prev
0.013498891 0.007233071 0.001083141 0.443129295
crf_slope_prev_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Predict Prevalence LAD Level
Explore distributions
df_uk_onset_prev_laua %>% ggplot(aes(onset_prev)) + geom_histogram()
df_uk_slope_prev_laua %>% ggplot(aes(slope_prev)) + geom_histogram()
Predict COVID onset with time-to-event regression
# predict onset from personality
cox_onset_prev_laua <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_onset_prev_laua)
cox_onset_prev_laua %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n, data = df_uk_onset_prev_laua)
n= 312, number of events= 312
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.11799 1.12524 0.07472 1.579 0.1143
pers_c -0.17400 0.84030 0.07851 -2.216 0.0267 *
pers_e 0.20837 1.23167 0.07505 2.776 0.0055 **
pers_a 0.05375 1.05522 0.06484 0.829 0.4071
pers_n 0.01251 1.01259 0.07158 0.175 0.8613
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.1252 0.8887 0.9720 1.3027
pers_c 0.8403 1.1901 0.7205 0.9801
pers_e 1.2317 0.8119 1.0632 1.4269
pers_a 1.0552 0.9477 0.9293 1.1982
pers_n 1.0126 0.9876 0.8800 1.1651
Concordance= 0.602 (se = 0.02 )
Likelihood ratio test= 27.18 on 5 df, p=5e-05
Wald test = 29.19 on 5 df, p=2e-05
Score (logrank) test = 29.1 on 5 df, p=2e-05
# predict onset from personality with controls
cox_onset_prev_laua_ctrl <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative,
data = df_uk_onset_prev_laua)
cox_onset_prev_laua_ctrl %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n + airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage + conservative,
data = df_uk_onset_prev_laua)
n= 312, number of events= 312
coef exp(coef) se(coef) z Pr(>|z|)
pers_o -0.0341890 0.9663888 0.1137888 -0.300 0.763826
pers_c -0.0376070 0.9630914 0.1059875 -0.355 0.722721
pers_e 0.0811992 1.0845869 0.0807194 1.006 0.314443
pers_a 0.0133296 1.0134188 0.0815961 0.163 0.870234
pers_n -0.0004303 0.9995697 0.0781902 -0.006 0.995609
airport_dist -0.0295206 0.9709108 0.0739004 -0.399 0.689550
males -0.2387449 0.7876157 0.0701625 -3.403 0.000667 ***
popdens 0.2236609 1.2506468 0.1028405 2.175 0.029643 *
manufacturing -0.1338256 0.8747426 0.0770596 -1.737 0.082449 .
tourism 0.0758201 1.0787685 0.0752577 1.007 0.313707
health -0.0853930 0.9181514 0.0624438 -1.368 0.171463
academic 0.1458217 1.1569899 0.1170275 1.246 0.212747
medinc -0.1750250 0.8394361 0.0913846 -1.915 0.055460 .
medage -0.2801971 0.7556348 0.1237521 -2.264 0.023563 *
conservative -0.1640329 0.8487141 0.1402000 -1.170 0.242004
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 0.9664 1.0348 0.7732 1.2078
pers_c 0.9631 1.0383 0.7824 1.1855
pers_e 1.0846 0.9220 0.9259 1.2705
pers_a 1.0134 0.9868 0.8636 1.1892
pers_n 0.9996 1.0004 0.8575 1.1651
airport_dist 0.9709 1.0300 0.8400 1.1222
males 0.7876 1.2697 0.6864 0.9037
popdens 1.2506 0.7996 1.0223 1.5299
manufacturing 0.8747 1.1432 0.7521 1.0174
tourism 1.0788 0.9270 0.9308 1.2502
health 0.9182 1.0891 0.8124 1.0377
academic 1.1570 0.8643 0.9198 1.4553
medinc 0.8394 1.1913 0.7018 1.0041
medage 0.7556 1.3234 0.5929 0.9631
conservative 0.8487 1.1783 0.6448 1.1171
Concordance= 0.649 (se = 0.018 )
Likelihood ratio test= 71.42 on 15 df, p=2e-09
Wald test = 81.62 on 15 df, p=4e-11
Score (logrank) test = 87.17 on 15 df, p=3e-12
Predict prevalence slopes with linear models
# predict slopes from personality
lm_slope_prev_laua <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_slope_prev_laua)
lm_slope_prev_laua %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_uk_slope_prev_laua)
Residuals:
Min 1Q Median 3Q Max
-4.2133 -0.4129 0.1032 0.5263 2.2010
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.413e-16 5.283e-02 0.000 1.00000
pers_o -2.208e-01 7.310e-02 -3.020 0.00274 **
pers_c -4.870e-01 7.765e-02 -6.272 1.22e-09 ***
pers_e 8.785e-02 7.165e-02 1.226 0.22108
pers_a 4.990e-02 7.034e-02 0.709 0.47859
pers_n -7.062e-02 7.215e-02 -0.979 0.32846
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9331 on 306 degrees of freedom
Multiple R-squared: 0.1432, Adjusted R-squared: 0.1292
F-statistic: 10.23 on 5 and 306 DF, p-value: 4.425e-09
lm_slope_prev_laua %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.08716012 0.08716012
pers_o -0.34138810 -0.10016782
pers_c -0.61509638 -0.35888294
pers_e -0.03035627 0.20605391
pers_a -0.06615124 0.16595921
pers_n -0.18966710 0.04841944
# predict slopes from personality with controls
lm_slope_prev_laua_ctrl <- lm(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev,
data = df_uk_slope_prev_laua)
lm_slope_prev_laua_ctrl %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n + airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage + conservative +
onset_prev, data = df_uk_slope_prev_laua)
Residuals:
Min 1Q Median 3Q Max
-2.36587 -0.47934 0.07511 0.48233 2.05828
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.157e-15 4.388e-02 0.000 1.000000
pers_o -1.082e-01 8.889e-02 -1.217 0.224581
pers_c -3.216e-01 8.221e-02 -3.912 0.000114 ***
pers_e 5.252e-02 6.319e-02 0.831 0.406543
pers_a 5.110e-02 6.473e-02 0.789 0.430500
pers_n -6.529e-02 6.455e-02 -1.012 0.312594
airport_dist -1.143e-01 5.733e-02 -1.994 0.047023 *
males -2.664e-01 5.906e-02 -4.510 9.36e-06 ***
popdens 1.996e-01 8.221e-02 2.428 0.015780 *
manufacturing -6.376e-02 5.946e-02 -1.072 0.284432
tourism -6.622e-04 6.027e-02 -0.011 0.991240
health 3.748e-02 5.184e-02 0.723 0.470214
academic 5.999e-02 9.881e-02 0.607 0.544258
medinc -8.994e-02 6.897e-02 -1.304 0.193197
medage -2.384e-01 9.994e-02 -2.386 0.017679 *
conservative 7.081e-02 1.133e-01 0.625 0.532461
onset_prev 5.111e-01 4.750e-02 10.761 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7751 on 295 degrees of freedom
Multiple R-squared: 0.4301, Adjusted R-squared: 0.3992
F-statistic: 13.91 on 16 and 295 DF, p-value: < 2.2e-16
lm_slope_prev_laua_ctrl %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.07240974 0.07240974
pers_o -0.25485049 0.03849370
pers_c -0.45728147 -0.18596760
pers_e -0.05173975 0.15677729
pers_a -0.05570562 0.15790033
pers_n -0.17179645 0.04121405
airport_dist -0.20895058 -0.01974658
males -0.36381053 -0.16891665
popdens 0.06395855 0.33526073
manufacturing -0.16187386 0.03434819
tourism -0.10010886 0.09878437
health -0.04805089 0.12301281
academic -0.10305098 0.22302120
medinc -0.20374181 0.02385425
medage -0.40333985 -0.07352087
conservative -0.11614187 0.25776935
onset_prev 0.43277274 0.58952577
CRF predicting slopes
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_slope_prev_laua <- cforest(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev,
data = df_uk_slope_prev,
controls = ctrls)
crf_slope_prev_varimp <- varimp(crf_slope_prev_laua, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev_laua, conditional = T, nperm = 1)
crf_slope_prev_varimp
pers_o pers_c pers_e pers_a pers_n airport_dist
6.004034e-03 2.052057e-02 -2.477124e-03 -5.399878e-03 -1.560624e-03 5.323515e-02
males popdens manufacturing tourism health academic
-1.243299e-03 1.419716e-02 3.355199e-04 1.913226e-02 -5.297063e-04 -1.180278e-03
medinc medage conservative onset_prev
1.182686e-02 2.453000e-03 -1.151113e-05 5.313719e-01
crf_slope_prev_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond
pers_o pers_c pers_e pers_a pers_n airport_dist
0.0081269597 0.0162285119 0.0002770556 -0.0032828696 -0.0007791452 0.0492546778
males popdens manufacturing tourism health academic
-0.0012493988 0.0162960501 0.0026788967 0.0173285064 0.0030926219 0.0048356668
medinc medage conservative onset_prev
0.0095891577 0.0054089674 0.0018216539 0.4410251062
crf_slope_prev_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Predict Social Distancing
Change point analysis
# keep only counties with full data
nuts_complete <- df_uk_socdist_scaled %>%
group_by(nuts3) %>%
summarize(n = n()) %>%
filter(n==max(.$n)) %>%
.$nuts3
# run changepoint analysis
df_uk_socdist_cpt_results <- df_uk_socdist_scaled %>%
select(nuts3, socdist_single_tile) %>%
filter(nuts3 %in% nuts_complete) %>%
split(.$nuts3) %>%
map(~ cpt.meanvar(as.vector(.$socdist_single_tile),
#penalty = 'Asymptotic',
class=TRUE,
param.estimates=TRUE,
Q=1,
test.stat = 'Normal'))
df_uk_socdist_cpt_results_2 <- df_uk_socdist_scaled %>%
select(nuts3, socdist_tiles) %>%
filter(nuts3 %in% nuts_complete) %>%
split(.$nuts3) %>%
map(~ cpt.meanvar(as.vector(.$socdist_tiles),
#penalty = 'Asymptotic',
class=TRUE,
param.estimates=TRUE,
Q=1,
test.stat = 'Normal'))
# calculate change point
df_uk_socdist_cpt_day <- df_uk_socdist_cpt_results %>%
map(cpts) %>%
unlist() %>%
as.data.frame() %>%
rename(cpt_day_socdist = '.') %>%
rownames_to_column('nuts3')
df_uk_socdist_cpt_day_2 <- df_uk_socdist_cpt_results_2 %>%
map(cpts) %>%
unlist() %>%
as.data.frame() %>%
rename(cpt_day_socdist_2 = '.') %>%
rownames_to_column('nuts3')
# calculate mean differences
df_uk_socdist_cpt_mean_diff <- df_uk_socdist_cpt_results %>%
map(param.est) %>%
map(~ .$mean) %>%
map(~ .[2]) %>%
unlist() %>%
as.data.frame() %>%
rename(mean_diff_socdist = '.') %>%
rownames_to_column('nuts3')
df_uk_socdist_cpt_mean_diff_2 <- df_uk_socdist_cpt_results_2 %>%
map(param.est) %>%
map(~ .$mean) %>%
map(~ -.[2]) %>%
unlist() %>%
as.data.frame() %>%
rename(mean_diff_socdist_2 = '.') %>%
rownames_to_column('nuts3')
# calculate means
df_uk_socdist_mean <- df_uk_socdist_scaled %>%
group_by(nuts3) %>%
summarize(mean_socdist = mean(socdist_single_tile))
df_uk_socdist_mean_2 <- df_uk_socdist_scaled %>%
group_by(nuts3) %>%
summarize(mean_socdist_2 = -mean(socdist_tiles))
# merge with county data
nuts_ut_key <- read_csv('nuts3_ut.csv')
Parsed with column specification:
cols(
nuts3 = [31mcol_character()[39m,
ut_area = [31mcol_character()[39m
)
df_uk_cpt_socdist <- df_uk_socdist_scaled %>%
select(-time, -socdist_single_tile, -socdist_tiles) %>%
distinct() %>%
left_join(df_uk_socdist_cpt_day, by='nuts3') %>%
left_join(df_uk_socdist_cpt_day_2, by='nuts3') %>%
left_join(df_uk_socdist_cpt_mean_diff, by='nuts3') %>%
left_join(df_uk_socdist_cpt_mean_diff_2, by='nuts3') %>%
left_join(df_ger_socdist_mean, by='nuts3') %>%
left_join(df_ger_socdist_mean_2, by='nuts3') %>%
left_join(nuts_ut_key, by='nuts3') %>%
left_join(select(df_uk_onset_prev, ut_area, onset_prev), by='ut_area') %>%
left_join(select(df_uk_slope_prev, ut_area, slope_prev), by='ut_area') %>%
select(-ut_area)
Error in tbl_vars_dispatch(x) : object 'df_ger_socdist_mean' not found
df_uk_cpt_socdist$cpt_day_socdist %>% hist()

df_uk_cpt_socdist$mean_diff_socdist %>% hist()

df_uk_cpt_socdist$mean_socdist %>% hist()

df_uk_cpt_socdist$cpt_day_socdist_2 %>% hist()

df_uk_cpt_socdist$mean_diff_socdist_2 %>% hist()

df_uk_cpt_socdist$mean_socdist_2 %>% hist()

cor(df_uk_cpt_socdist$mean_diff_socdist,
df_uk_cpt_socdist$mean_socdist)
[,1]
[1,] 0.9756932
cor(df_uk_cpt_socdist$mean_diff_socdist_2,
df_uk_cpt_socdist$mean_socdist_2)
[,1]
[1,] 0.9855565
for(i in head(df_uk_socdist_cpt_results_2, 5)){
plot(i)
}




NA

for(i in head(df_uk_socdist_cpt_results_2, 5)){
plot(i)
}
Predicting change points with time-to-event regression
# predict hazard from personality
cox_cpt_socdist <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_cpt_socdist)
cox_cpt_socdist %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n, data = df_uk_cpt_socdist)
n= 131, number of events= 131
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.037677 1.038396 0.159307 0.237 0.813
pers_c 0.006832 1.006855 0.144736 0.047 0.962
pers_e 0.002145 1.002148 0.160157 0.013 0.989
pers_a -0.054244 0.947201 0.134632 -0.403 0.687
pers_n -0.034435 0.966152 0.130787 -0.263 0.792
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.0384 0.9630 0.7599 1.419
pers_c 1.0069 0.9932 0.7582 1.337
pers_e 1.0021 0.9979 0.7322 1.372
pers_a 0.9472 1.0557 0.7275 1.233
pers_n 0.9662 1.0350 0.7477 1.248
Concordance= 0.941 (se = 0.033 )
Likelihood ratio test= 0.82 on 5 df, p=1
Wald test = 0.86 on 5 df, p=1
Score (logrank) test = 0.86 on 5 df, p=1
# predict hazard from personality with controls
cox_cpt_socdist_ctrl <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev,
data = df_uk_cpt_socdist)
cox_cpt_socdist_ctrl %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n + airport_dist + males + popdens +
manufacturing + tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev, data = df_uk_cpt_socdist)
n= 130, number of events= 130
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o -0.054009 0.947424 0.259655 -0.208 0.835
pers_c 0.015151 1.015266 0.207295 0.073 0.942
pers_e -0.011907 0.988164 0.169616 -0.070 0.944
pers_a -0.038884 0.961862 0.168035 -0.231 0.817
pers_n -0.036467 0.964189 0.145252 -0.251 0.802
airport_dist 0.027010 1.027378 0.124971 0.216 0.829
males -0.065868 0.936255 0.163367 -0.403 0.687
popdens 0.133712 1.143063 0.239108 0.559 0.576
manufacturing 0.052628 1.054038 0.140610 0.374 0.708
tourism 0.017427 1.017579 0.131044 0.133 0.894
health -0.026273 0.974069 0.119258 -0.220 0.826
academic 0.055605 1.057180 0.267586 0.208 0.835
medinc -0.049497 0.951708 0.168257 -0.294 0.769
medage -0.051444 0.949857 0.232922 -0.221 0.825
conservative -0.052351 0.948996 0.288138 -0.182 0.856
onset_prev 0.001703 1.001704 0.015047 0.113 0.910
slope_prev -0.021361 0.978865 0.126324 -0.169 0.866
exp(coef) exp(-coef) lower .95 upper .95
pers_o 0.9474 1.0555 0.5695 1.576
pers_c 1.0153 0.9850 0.6763 1.524
pers_e 0.9882 1.0120 0.7087 1.378
pers_a 0.9619 1.0397 0.6920 1.337
pers_n 0.9642 1.0371 0.7253 1.282
airport_dist 1.0274 0.9734 0.8042 1.313
males 0.9363 1.0681 0.6797 1.290
popdens 1.1431 0.8748 0.7154 1.826
manufacturing 1.0540 0.9487 0.8001 1.388
tourism 1.0176 0.9827 0.7871 1.316
health 0.9741 1.0266 0.7710 1.231
academic 1.0572 0.9459 0.6257 1.786
medinc 0.9517 1.0507 0.6844 1.324
medage 0.9499 1.0528 0.6017 1.499
conservative 0.9490 1.0537 0.5395 1.669
onset_prev 1.0017 0.9983 0.9726 1.032
slope_prev 0.9789 1.0216 0.7642 1.254
Concordance= 0.97 (se = 0.015 )
Likelihood ratio test= 1.43 on 17 df, p=1
Wald test = 1.51 on 17 df, p=1
Score (logrank) test = 1.52 on 17 df, p=1
Linear models predicting mean differences
lm_meandiff_socdist <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_cpt_socdist)
lm_meandiff_socdist %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_uk_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-1.59230 -0.47844 -0.01606 0.42775 1.91779
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.476e-15 6.443e-02 0.000 1.00000
pers_o 1.733e-01 1.176e-01 1.474 0.14308
pers_c -1.721e-01 1.076e-01 -1.600 0.11221
pers_e 3.030e-01 1.138e-01 2.662 0.00878 **
pers_a -1.883e-01 9.469e-02 -1.988 0.04895 *
pers_n -1.439e-02 9.386e-02 -0.153 0.87842
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7375 on 125 degrees of freedom
Multiple R-squared: 0.4771, Adjusted R-squared: 0.4562
F-statistic: 22.81 on 5 and 125 DF, p-value: 3.272e-16
lm_meandiff_socdist %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.10677263 0.106772634
pers_o -0.02157286 0.368159717
pers_c -0.35034428 0.006189375
pers_e 0.11439435 0.491547403
pers_a -0.34520521 -0.031373565
pers_n -0.16992488 0.141149343
lm_meandiff_socdist_ctrl <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev,
data = df_uk_cpt_socdist)
lm_meandiff_socdist_ctrl %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n + airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage + conservative +
onset_prev + slope_prev, data = df_uk_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-1.64573 -0.27449 0.03543 0.37169 1.53058
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.335186 0.291520 1.150 0.252680
pers_o 0.038823 0.158645 0.245 0.807122
pers_c -0.134104 0.126954 -1.056 0.293095
pers_e 0.074261 0.103326 0.719 0.473818
pers_a 0.193685 0.100280 1.931 0.055955 .
pers_n 0.121583 0.086393 1.407 0.162099
airport_dist 0.055364 0.075845 0.730 0.466939
males -0.224919 0.089231 -2.521 0.013123 *
popdens 0.579669 0.143703 4.034 0.000101 ***
manufacturing -0.232107 0.083830 -2.769 0.006587 **
tourism 0.093210 0.079389 1.174 0.242847
health 0.012968 0.069975 0.185 0.853309
academic -0.020513 0.156637 -0.131 0.896043
medinc 0.410681 0.103757 3.958 0.000133 ***
medage -0.049986 0.137375 -0.364 0.716646
conservative 0.162858 0.174256 0.935 0.352009
onset_prev -0.010575 0.009011 -1.174 0.243046
slope_prev 0.038356 0.076465 0.502 0.616922
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6077 on 112 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.6797, Adjusted R-squared: 0.6311
F-statistic: 13.98 on 17 and 112 DF, p-value: < 2.2e-16
lm_meandiff_socdist_ctrl %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.14832058 0.818692212
pers_o -0.22430100 0.301947876
pers_c -0.34466665 0.076458268
pers_e -0.09711227 0.245633955
pers_a 0.02736364 0.360006734
pers_n -0.02170550 0.264871037
airport_dist -0.07043049 0.181157779
males -0.37291591 -0.076922591
popdens 0.34132736 0.818010783
manufacturing -0.37114475 -0.093069228
tourism -0.03846205 0.224882948
health -0.10309016 0.129026269
academic -0.28030743 0.239281293
medinc 0.23859252 0.582770261
medage -0.27783248 0.177860983
conservative -0.12615763 0.451873581
onset_prev -0.02551965 0.004369936
slope_prev -0.08846702 0.165179585
CRF predicting mean difference
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_meandiff_socdist <- cforest(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev,
data = df_uk_cpt_socdist %>% drop_na(),
controls = ctrls)
crf_meandiff_socdist_varimp <- varimp(crf_meandiff_socdist, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist, conditional = T, nperm = 1)
crf_meandiff_socdist_varimp
pers_o pers_c pers_e pers_a pers_n
0.0866398614 0.0139129422 0.0448894610 0.0243862299 0.0010570870
airport_dist males popdens manufacturing tourism
0.0006984125 0.0063141561 0.2844528841 0.0896169809 -0.0019615256
health academic medinc medage conservative
0.0005118732 0.0287680803 0.1190340860 0.0127496646 0.0424411558
onset_prev slope_prev
0.0024072003 0.0020732020
crf_meandiff_socdist_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond
pers_o pers_c pers_e pers_a pers_n
0.0827213621 0.0150431607 0.0415189305 0.0215636149 0.0009602196
airport_dist males popdens manufacturing tourism
0.0008033941 0.0053098944 0.2567242510 0.0940480335 -0.0007021642
health academic medinc medage conservative
0.0009865419 0.0316531387 0.1111877822 0.0136577443 0.0394662118
onset_prev slope_prev
0.0029093532 0.0004324219
crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Predicting change points with time-to-event regression
# predict hazard from personality
cox_cpt_socdist_2 <- coxph(Surv(cpt_day_socdist_2, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_cpt_socdist)
cox_cpt_socdist_2 %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist_2, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n, data = df_uk_cpt_socdist)
n= 131, number of events= 131
coef exp(coef) se(coef) z Pr(>|z|)
pers_o -0.050902 0.950372 0.156351 -0.326 0.745
pers_c -0.060242 0.941537 0.149829 -0.402 0.688
pers_e 0.087182 1.091095 0.162827 0.535 0.592
pers_a 0.103047 1.108543 0.131924 0.781 0.435
pers_n 0.003533 1.003539 0.133928 0.026 0.979
exp(coef) exp(-coef) lower .95 upper .95
pers_o 0.9504 1.0522 0.6995 1.291
pers_c 0.9415 1.0621 0.7019 1.263
pers_e 1.0911 0.9165 0.7930 1.501
pers_a 1.1085 0.9021 0.8560 1.436
pers_n 1.0035 0.9965 0.7719 1.305
Concordance= 0.637 (se = 0.136 )
Likelihood ratio test= 0.92 on 5 df, p=1
Wald test = 0.93 on 5 df, p=1
Score (logrank) test = 0.92 on 5 df, p=1
# predict hazard from personality with controls
cox_cpt_socdist_ctrl_2 <- coxph(Surv(cpt_day_socdist_2, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev,
data = df_uk_cpt_socdist)
cox_cpt_socdist_ctrl_2 %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist_2, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n + airport_dist + males + popdens +
manufacturing + tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev, data = df_uk_cpt_socdist)
n= 130, number of events= 130
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o -0.1128691 0.8932676 0.2670282 -0.423 0.673
pers_c -0.0342248 0.9663542 0.2083320 -0.164 0.870
pers_e 0.1041443 1.1097605 0.1768710 0.589 0.556
pers_a 0.0010809 1.0010814 0.1670260 0.006 0.995
pers_n -0.0187305 0.9814438 0.1473178 -0.127 0.899
airport_dist -0.0469830 0.9541036 0.1294395 -0.363 0.717
males 0.0373559 1.0380624 0.1579103 0.237 0.813
popdens -0.0146463 0.9854604 0.2523392 -0.058 0.954
manufacturing 0.1499156 1.1617362 0.1336777 1.121 0.262
tourism 0.0253305 1.0256541 0.1308250 0.194 0.846
health 0.1119038 1.1184053 0.1163634 0.962 0.336
academic -0.0233304 0.9769397 0.2648776 -0.088 0.930
medinc 0.0002381 1.0002381 0.1738722 0.001 0.999
medage 0.1011705 1.1064653 0.2282495 0.443 0.658
conservative -0.1329131 0.8755412 0.3036219 -0.438 0.662
onset_prev 0.0010598 1.0010604 0.0153327 0.069 0.945
slope_prev 0.0194326 1.0196227 0.1327037 0.146 0.884
exp(coef) exp(-coef) lower .95 upper .95
pers_o 0.8933 1.1195 0.5293 1.508
pers_c 0.9664 1.0348 0.6424 1.454
pers_e 1.1098 0.9011 0.7847 1.570
pers_a 1.0011 0.9989 0.7216 1.389
pers_n 0.9814 1.0189 0.7353 1.310
airport_dist 0.9541 1.0481 0.7403 1.230
males 1.0381 0.9633 0.7617 1.415
popdens 0.9855 1.0148 0.6010 1.616
manufacturing 1.1617 0.8608 0.8940 1.510
tourism 1.0257 0.9750 0.7937 1.325
health 1.1184 0.8941 0.8903 1.405
academic 0.9769 1.0236 0.5813 1.642
medinc 1.0002 0.9998 0.7114 1.406
medage 1.1065 0.9038 0.7074 1.731
conservative 0.8755 1.1422 0.4829 1.588
onset_prev 1.0011 0.9989 0.9714 1.032
slope_prev 1.0196 0.9808 0.7861 1.323
Concordance= 0.788 (se = 0.127 )
Likelihood ratio test= 4.62 on 17 df, p=1
Wald test = 4.63 on 17 df, p=1
Score (logrank) test = 4.64 on 17 df, p=1
Linear models predicting mean differences
lm_meandiff_socdist_2 <- lm(mean_diff_socdist_2 ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_uk_cpt_socdist)
lm_meandiff_socdist_2 %>% summary()
Call:
lm(formula = mean_diff_socdist_2 ~ pers_o + pers_c + pers_e +
pers_a + pers_n, data = df_uk_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-2.22275 -0.46068 -0.02762 0.45220 1.96182
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.119e-15 5.933e-02 0.000 1.00000
pers_o 2.500e-02 1.083e-01 0.231 0.81776
pers_c -1.594e-01 9.905e-02 -1.609 0.11018
pers_e 4.230e-01 1.048e-01 4.037 9.39e-05 ***
pers_a -2.424e-01 8.719e-02 -2.781 0.00627 **
pers_n -1.862e-01 8.642e-02 -2.155 0.03309 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.679 on 125 degrees of freedom
Multiple R-squared: 0.5566, Adjusted R-squared: 0.5389
F-statistic: 31.39 on 5 and 125 DF, p-value: < 2.2e-16
lm_meandiff_socdist_2 %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.09831548 0.09831548
pers_o -0.15442886 0.20443410
pers_c -0.32350683 0.00478680
pers_e 0.24932419 0.59660402
pers_a -0.38693082 -0.09795689
pers_n -0.32944669 -0.04301177
lm_meandiff_socdist_ctrl_2 <- lm(mean_diff_socdist_2 ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev,
data = df_uk_cpt_socdist)
lm_meandiff_socdist_ctrl_2 %>% summary()
Call:
lm(formula = mean_diff_socdist_2 ~ pers_o + pers_c + pers_e +
pers_a + pers_n + airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage + conservative +
onset_prev + slope_prev, data = df_uk_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-1.3539 -0.2995 0.0411 0.3338 1.1103
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.630291 0.234206 2.691 0.008211 **
pers_o -0.023938 0.127455 -0.188 0.851359
pers_c -0.047208 0.101995 -0.463 0.644371
pers_e 0.106542 0.083012 1.283 0.201980
pers_a 0.094985 0.080565 1.179 0.240903
pers_n -0.036763 0.069408 -0.530 0.597387
airport_dist -0.095928 0.060934 -1.574 0.118239
males -0.248234 0.071688 -3.463 0.000758 ***
popdens 0.609775 0.115451 5.282 6.36e-07 ***
manufacturing -0.128026 0.067349 -1.901 0.059880 .
tourism -0.073726 0.063781 -1.156 0.250175
health -0.038582 0.056218 -0.686 0.493945
academic 0.271148 0.125842 2.155 0.033330 *
medinc 0.158529 0.083358 1.902 0.059770 .
medage -0.052320 0.110367 -0.474 0.636385
conservative 0.220453 0.139997 1.575 0.118145
onset_prev -0.019925 0.007239 -2.752 0.006902 **
slope_prev 0.138325 0.061432 2.252 0.026293 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4882 on 112 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.7926, Adjusted R-squared: 0.7612
F-statistic: 25.18 on 17 and 112 DF, p-value: < 2.2e-16
lm_meandiff_socdist_ctrl_2 %>% confint(level=0.9)
5 % 95 %
(Intercept) 0.24184319 1.018739321
pers_o -0.23533201 0.187455280
pers_c -0.21637348 0.121957429
pers_e -0.03113864 0.244222991
pers_a -0.03863790 0.228606898
pers_n -0.15188087 0.078354149
airport_dist -0.19699032 0.005135194
males -0.36713412 -0.129333676
popdens 0.41829143 0.801257933
manufacturing -0.23972830 -0.016322990
tourism -0.17951095 0.032059895
health -0.13182278 0.054659088
academic 0.06242939 0.479865921
medinc 0.02027300 0.296784706
medage -0.23537132 0.130731893
conservative -0.01174127 0.452647845
onset_prev -0.03193196 -0.007918731
slope_prev 0.03643544 0.240214614
CRF predicting mean difference
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_meandiff_socdist_2 <- cforest(mean_diff_socdist_2 ~
pers_o + pers_c + pers_e + pers_a + pers_n +
airport_dist + males + popdens + manufacturing +
tourism + health + academic + medinc + medage +
conservative + onset_prev + slope_prev,
data = df_uk_cpt_socdist %>% drop_na(),
controls = ctrls)
crf_meandiff_socdist_varimp_ <- varimp(crf_meandiff_socdist_2, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist_2, conditional = T, nperm = 1)
crf_meandiff_socdist_varimp
pers_o pers_c pers_e pers_a pers_n
0.0866398614 0.0139129422 0.0448894610 0.0243862299 0.0010570870
airport_dist males popdens manufacturing tourism
0.0006984125 0.0063141561 0.2844528841 0.0896169809 -0.0019615256
health academic medinc medage conservative
0.0005118732 0.0287680803 0.1190340860 0.0127496646 0.0424411558
onset_prev slope_prev
0.0024072003 0.0020732020
crf_meandiff_socdist_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond
pers_o pers_c pers_e pers_a pers_n
0.0105084218 0.0004282805 0.1288694275 0.0030573110 0.0016755109
airport_dist males popdens manufacturing tourism
0.0579376000 0.0012207368 0.1855763980 0.0573411043 0.0032837033
health academic medinc medage conservative
0.0006151607 0.1741166028 0.0934911787 0.0053397776 0.0714661695
onset_prev slope_prev
0.0058773245 0.0008061143
crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Export data
uk_list_results <- list(cox_onset_prev_laua, cox_onset_prev_laua_ctrl,
lm_slope_prev_laua, lm_slope_prev_laua_ctrl,
cox_cpt_socdist, cox_cpt_socdist_ctrl,
lm_meandiff_socdist, lm_meandiff_socdist_ctrl,
cox_cpt_socdist_2, cox_cpt_socdist_ctrl_2,
lm_meandiff_socdist_2, lm_meandiff_socdist_ctrl_2)
results_names <- list('cox_onset_prev', 'cox_onset_prev_ctrl',
'lm_slope_prev', 'lm_slope_prev_ctrl',
'cox_cpt_socdist', 'cox_cpt_socdist_ctrl',
'lm_meandiff_socdist', 'lm_meandiff_socdist_ctrl',
'cox_cpt_socdist_2', 'cox_cpt_socdist_ctrl_2',
'lm_meandiff_socdist_2', 'lm_meandiff_socdist_ctrl_2')
names(uk_list_results) <- results_names
save(uk_list_results, file="uk_list_results.RData")
write_csv(df_uk_slope_prev, '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Delivery/df_uk_slope_prev.csv')
write_csv(df_uk_cpt_socdist, '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Delivery/df_uk_cpt_socdist.csv')
---
title: "COVID19 UK"
author: "Heinrich Peters"
date: "4/23/2020"
output: html_notebook
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

# MAC
 knitr::opts_knit$set(root.dir = '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/UK')
 
library(lmerTest)
library(nlme)
library(psych)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(party)
library(doParallel)
library(changepoint)
library(survival)
library(survminer)

```

# Prepare data

### Read and format data

### Prevalence 

```{r}
df_uk_prev <- read_csv('UK_timeseries_prep_2005.csv')

df_uk_prev <- df_uk_prev %>% 
  select(ut_area, date, rate) %>% 
  rename(rate_day = rate) %>%
  mutate(date = as.Date(date, "%d%b%Y"))



df_uk_prev2 <- read_csv('UK_timeseries_prep_0506_laua_all.csv')

df_uk_prev2 <- df_uk_prev2 %>% 
  select(laua, date, males, popdens, manufacturing, tourism, health,
         academic, medage, conservative, airport_dist, medinc, open,
         extra, agree, neuro, sci, rate) %>%
  rename(pers_o = open, 
         pers_c = sci,
         pers_e = extra,
         pers_a = agree,
         pers_n = neuro,
         rate_day = rate) %>%
  mutate(date = as.Date(date, "%d%b%Y"))

df_uk_prev2 %>% select(-date, -rate_day) %>% 
  distinct() %>% write_csv('df_uk_pers_laua.csv')

df_uk_prev2 %>% select(laua, date, rate_day) %>% write_csv('uk_prev_laua.csv')
  
```

### Personality
```{r}

df_uk_pers <- read_csv('timeseries_uk_utla_march9_april_09.csv')

df_uk_pers <- df_uk_pers %>% 
  select(ut_area, open, agree, neuro, sci, extra) %>% 
  dplyr::rename(pers_o = open, 
         pers_c = sci,
         pers_e = extra,
         pers_a = agree,
         pers_n = neuro) %>%
  distinct()

df_uk_pers %>% write_csv('df_uk_pers_ut.csv')

df_uk_pers_nuts <- read_csv('UK_socdist_fb_nuts3.csv')

df_uk_pers_nuts <- df_uk_pers_nuts %>% 
  select(nuts3, open, sci, extra, agree, neuro) %>%
  dplyr::rename(pers_o = open, 
                pers_c = sci,
                pers_e = extra,
                pers_a = agree,
                pers_n = neuro) %>%
  distinct()

df_uk_pers_nuts 

```

### Social distancing
```{r, warning=FALSE}

fb_files <- list.files('../FB Data/UK individual files',
                       '*.csv', full.names = T)

df_uk_socdist <- fb_files %>% 
  map(read.csv) %>% bind_rows()

df_uk_socdist <- df_uk_socdist %>%
  select(ds, all_day_bing_tiles_visited_relative_change,
         all_day_ratio_single_tile_users, external_polygon_id) %>%
  rename(date = ds,
         nuts3 = external_polygon_id,
         socdist_tiles = all_day_bing_tiles_visited_relative_change,
         socdist_single_tile = all_day_ratio_single_tile_users) %>%
  mutate(nuts3 = as.character(nuts3),
         date = as.Date(date)) %>%
  arrange(nuts3, date) %>%
  drop_na()

```


```{r}
df_uk_socdist %>% select(nuts3) %>% distinct() %>% nrow()
```

### Controls 
```{r}
df_uk_ctrl_nuts <- read_csv("controls_UK_nuts3.csv")
df_uk_ctrl_nuts <- df_uk_ctrl_nuts %>% select(-nuts3_name)
df_uk_ctrl_nuts


df_uk_ctrl_ut <- read_csv("controls_UK_ut.csv")
df_uk_ctrl_ut <- df_uk_ctrl_ut %>% select(-ut_name)
df_uk_ctrl_ut


```





### Merge prevalence data 
```{r}
df_uk_prev <- df_uk_prev %>% 
  plyr::join(df_uk_pers, by='ut_area') %>% 
  plyr::join(df_uk_ctrl_ut, by='ut_area')

# create sequence of dates
date_sequence <- seq.Date(min(df_uk_prev$date),
                          max(df_uk_prev$date), 1)
                     
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')

# merge day index with gps data
df_uk_prev = df_uk_prev %>% 
  merge(df_dates, by='date') %>% 
  arrange(ut_area) %>%
  as_tibble()

df_uk_prev %>% select(-date, -rate_day, -time) %>% distinct() %>% write_csv('df_uk_pers_ut.csv')
  
```

```{r}

# create sequence of dates
date_sequence <- seq.Date(min(df_uk_prev2$date),
                          max(df_uk_prev2$date), 1)
                     
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')

# merge day index with gps data
df_uk_prev2 = df_uk_prev2 %>% 
  merge(df_dates, by='date') %>% 
  arrange(laua) %>%
  as_tibble()

df_uk_prev2

```



### Merge social distancing data
```{r}

df_uk_socdist <- df_uk_socdist %>% 
  plyr::join(df_uk_ctrl_nuts, by='nuts3') %>%
  plyr::join(df_uk_pers_nuts, by='nuts3')


# create sequence of dates
date_sequence <- seq.Date(min(df_uk_socdist$date),
                          as.Date('2020-04-28'), 1)
                     
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')

# merge day index with gps data
df_uk_socdist = df_uk_socdist %>% 
  inner_join(df_dates, by='date') %>% 
  arrange(nuts3) %>%
  as_tibble()


df_uk_socdist %>% select(-date, -socdist_tiles, -socdist_single_tile, -time) %>% distinct() %>% write_csv('df_uk_pers_nuts.csv')

```

### Check timeframes 
```{r}
df_uk_prev$date %>% summary()
df_uk_socdist$date %>% summary()
```

### Control for weekend effect in social distancing
```{r}

easter <- seq.Date(as.Date('2020-04-10'), as.Date('2020-04-13'), 1)


df_uk_loess <- df_uk_socdist %>% 
  mutate(weekday = format(date, '%u')) %>% 
  filter(!(weekday %in% c('6','7') | date %in% easter)) %>% 
  split(.$nuts3) %>%
  map(~ loess(socdist_single_tile ~ time, data = .)) %>%
  map(predict, 1:max(df_uk_socdist$time)) %>% 
  bind_rows() %>% 
  gather(key = 'nuts3', value = 'loess') %>% 
  group_by(nuts3) %>% 
  mutate(time = row_number())

df_uk_loess_2 <- df_uk_socdist %>% 
  mutate(weekday = format(date, '%u')) %>% 
  filter(!(weekday %in% c('6','7') | date %in% easter)) %>% 
  split(.$nuts3) %>%
  map(~ loess(socdist_tiles ~ time, data = .)) %>%
  map(predict, 1:max(df_uk_socdist$time)) %>% 
  bind_rows() %>% 
  gather(key = 'nuts3', value = 'loess') %>%
  rename(loess_2 = loess) %>%
  group_by(nuts3) %>% 
  mutate(time = row_number())

df_uk_socdist <- df_uk_socdist %>% 
  merge(df_uk_loess, by=c('nuts3', 'time')) %>% 
  merge(df_uk_loess_2, by=c('nuts3', 'time')) %>% 
  mutate(weekday = format(date, '%u')) %>% 
  mutate(socdist_single_tile_clean = ifelse(weekday %in% c('6','7') | date %in% easter, 
                                            loess, socdist_single_tile),
         socdist_tiles_clean = ifelse(weekday %in% c('6','7') | date %in% easter, 
                                            loess_2, socdist_tiles)) %>%
  arrange(nuts3, time) %>% 
  select(-weekday)

df_uk_socdist <- df_uk_socdist %>% drop_na() %>% mutate(time = time-1)

```

# Explore data

### Plot prevalence over time
```{r}

df_uk_prev %>% ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=ut_area, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_uk_prev %>% mutate(prev_tail = cut(.[[i]], 
                                       breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
                                       labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(prev_tail != 'center') %>%
  ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=ut_area, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~prev_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```

```{r}

df_uk_prev2 %>% ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=laua, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_uk_prev2 %>% mutate(prev_tail = cut(.[[i]], 
                                       breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
                                       labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(prev_tail != 'center') %>%
  ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=laua, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~prev_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```

### Plot social distancing over time
```{r}

df_uk_socdist %>% ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=nuts3, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall social distancing over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_uk_socdist %>% mutate(socdist_tail = cut(.[[i]], 
                                       breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
                                       labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(socdist_tail != 'center') %>%
  ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=nuts3, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~socdist_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```

```{r}

df_uk_socdist %>% ggplot(aes(x=time, y=socdist_tiles)) + 
  geom_point(aes(col=nuts3, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall social distancing over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_uk_socdist %>% mutate(socdist_tail = cut(.[[i]], 
                                       breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
                                       labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(socdist_tail != 'center') %>%
  ggplot(aes(x=time, y=socdist_tiles)) + 
  geom_point(aes(col=nuts3, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~socdist_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```


```{r}

df_uk_socdist <- df_uk_socdist %>% 
  mutate(socdist_single_tile = socdist_single_tile_clean,
         socdist_tiles = socdist_tiles_clean) %>% 
  select(-loess, -loess_2, -socdist_single_tile_clean, -socdist_tiles_clean)

```

### Correlations
```{r}

df_uk_prev %>% group_by(ut_area) %>% 
  summarize_if(is.numeric, mean, na.rm=T) %>% 
  select(-ut_area, -time) %>% 
  cor(use = 'pairwise.complete') %>% round(3) %>%
  as.data.frame()

df_uk_socdist %>% group_by(nuts3) %>% 
  summarize_if(is.numeric, mean, na.rm=T) %>% 
  select(-nuts3, -time) %>% 
  cor(use = 'pairwise.complete') %>% round(3) %>% 
  as.data.frame()

```


## Rescale Data
```{r}
lvl2_scaled_ut <- df_uk_prev %>% 
  dplyr::select(-time, -date, -rate_day) %>% 
  distinct() %>% 
  mutate_at(vars(-ut_area), scale)

lvl1_scaled_ut <- df_uk_prev %>% select(ut_area, time, rate_day)

df_uk_prev_scaled <- plyr::join(lvl1_scaled_ut, lvl2_scaled_ut, by = 'ut_area')

df_uk_prev_scaled
```

```{r}
lvl2_scaled_laua <- df_uk_prev2 %>% 
  dplyr::select(-time, -date, -rate_day) %>% 
  distinct() %>% 
  mutate_at(vars(-laua), scale)

lvl1_scaled_laua <- df_uk_prev2 %>% select(laua, time, rate_day)

df_uk_prev2_scaled <- plyr::join(lvl1_scaled_laua, lvl2_scaled_laua, by = 'laua')

df_uk_prev2_scaled

```

```{r}

lvl2_scaled_nuts <- df_uk_socdist %>% 
  dplyr::select(-time, -date, -socdist_tiles, -socdist_single_tile) %>% 
  distinct() %>% 
  mutate_at(vars(-nuts3), scale)

lvl1_scaled_nuts <- df_uk_socdist %>% 
  select(nuts3, time, socdist_single_tile, socdist_tiles) %>% 
  mutate_at(vars(-nuts3, -time), scale)

df_uk_socdist_scaled <- plyr::join(lvl1_scaled_nuts, lvl2_scaled_nuts, by = 'nuts3')

df_uk_socdist_scaled

```




# Predict Prevalence UT Level
### Extract first day of covid outbreak
```{r}

# get onset day
df_uk_onset_prev <- df_uk_prev_scaled %>% 
  group_by(ut_area) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  summarize(onset_prev = min(time))
  
# merge with county data
df_uk_onset_prev <- df_uk_prev_scaled %>% 
  select(-time, -rate_day) %>%
  distinct() %>% 
  left_join(df_uk_onset_prev, by = 'ut_area')

# handle censored data
df_uk_onset_prev <- df_uk_onset_prev %>% 
  mutate(event = ifelse(is.na(onset_prev), 0, 1)) %>% 
  mutate(onset_prev = replace_na(onset_prev, as.numeric(diff(range(df_uk_prev$date)))+1))

```

### Extract slopes
```{r}

# cut time series before onset
df_uk_prev_scaled <- df_uk_prev_scaled %>% 
  group_by(ut_area) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  mutate(time = time-min(time)+1) %>%
  ungroup() %>%
  filter(time <= 30) %>%
  select(-rate_cs)

# drop counties with little data
df_uk_prev_scaled <- df_uk_prev_scaled %>%
  group_by(ut_area) %>%
  filter(n() == 30) %>%
  ungroup()

# log transform prevalence data 
df_uk_prev_scaled <- df_uk_prev_scaled %>% 
  mutate(rate_day = log(rate_day))

# extract slope prevalence
df_uk_slope_prev <- df_uk_prev_scaled %>% split(.$ut_area) %>% 
  map(~ lm(rate_day ~ time, data = .)) %>%
  map(coef) %>% 
  map_dbl('time') %>% 
  as.data.frame() %>% 
  rownames_to_column('ut_area') %>% 
  rename(slope_prev = '.')

# merge with county data
df_uk_slope_prev <- df_uk_onset_prev %>% 
  inner_join(df_uk_slope_prev, by = 'ut_area') %>%
  drop_na()

# standardize slopes
df_uk_slope_prev <- df_uk_slope_prev %>% 
  mutate(slope_prev = scale(slope_prev),
         onset_prev = scale(onset_prev))
```


### Explore distributions
```{r}

df_uk_onset_prev %>% ggplot(aes(onset_prev)) + geom_histogram()
df_uk_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram()

```


## Predict COVID onset with time-to-event regression 
```{r}

# predict onset from personality
cox_onset_prev <- coxph(Surv(onset_prev, event) ~ 
                          pers_o + pers_c + pers_e + pers_a + pers_n, 
                        data = df_uk_onset_prev)
cox_onset_prev %>% summary()

# predict onset from personality with controls
cox_onset_prev_ctrl <- coxph(Surv(onset_prev, event) ~ 
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative,
                             data = df_uk_onset_prev)
cox_onset_prev_ctrl %>% summary()

```


## Predict prevalence slopes with linear models
```{r}

# predict slopes from personality
lm_slope_prev <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_uk_slope_prev)
lm_slope_prev %>% summary()
lm_slope_prev %>% confint(level=0.9)

# predict slopes from personality with controls
lm_slope_prev_ctrl <- lm(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev,
                         data = df_uk_slope_prev)
lm_slope_prev_ctrl %>% summary()
lm_slope_prev_ctrl %>% confint(level=0.9)

```

### CRF predicting slopes
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_slope_prev <- cforest(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev,
                           data = df_uk_slope_prev, 
                         controls = ctrls)

crf_slope_prev_varimp <- varimp(crf_slope_prev, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev, conditional = T, nperm = 1)

crf_slope_prev_varimp
crf_slope_prev_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond
crf_slope_prev_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```


# Predict Prevalence LAD Level
### Extract first day of covid outbreak
```{r}

# get onset day
df_uk_onset_prev_laua <- df_uk_prev2_scaled %>% 
  group_by(laua) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  summarize(onset_prev = min(time))
  
# merge with county data
df_uk_onset_prev_laua <- df_uk_prev2_scaled %>% 
  select(-time, -rate_day) %>%
  distinct() %>% 
  left_join(df_uk_onset_prev_laua, by = 'laua')

# handle censored data
df_uk_onset_prev_laua <- df_uk_onset_prev_laua %>% 
  mutate(event = ifelse(is.na(onset_prev), 0, 1)) %>% 
  mutate(onset_prev = replace_na(onset_prev, as.numeric(diff(range(df_uk_prev2$date)))+1))

```

### Extract slopes
```{r}

# cut time series before onset
df_uk_prev2_scaled <- df_uk_prev2_scaled %>% 
  group_by(laua) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  mutate(time = time-min(time)+1) %>%
  ungroup() %>%
  filter(time <= 30) %>%
  select(-rate_cs)

# drop counties with little data
df_uk_prev2_scaled <- df_uk_prev2_scaled %>%
  group_by(laua) %>%
  filter(n() == 30) %>%
  ungroup()

# log transform prevalence data 
df_uk_prev2_scaled <- df_uk_prev2_scaled %>% 
  mutate(rate_day = log(rate_day))

# extract slope prevalence
df_uk_slope_prev_laua <- df_uk_prev2_scaled %>% split(.$laua) %>% 
  map(~ lm(rate_day ~ time, data = .)) %>%
  map(coef) %>% 
  map_dbl('time') %>% 
  as.data.frame() %>% 
  rownames_to_column('laua') %>% 
  rename(slope_prev = '.')

# merge with county data
df_uk_slope_prev_laua <- df_uk_onset_prev_laua %>% 
  inner_join(df_uk_slope_prev_laua, by = 'laua') %>%
  drop_na()

# standardize slopes
df_uk_slope_prev_laua <- df_uk_slope_prev_laua %>% 
  mutate(slope_prev = scale(slope_prev),
         onset_prev = scale(onset_prev))
```


### Explore distributions
```{r}

df_uk_onset_prev_laua %>% ggplot(aes(onset_prev)) + geom_histogram()
df_uk_slope_prev_laua %>% ggplot(aes(slope_prev)) + geom_histogram()

```


## Predict COVID onset with time-to-event regression 
```{r}

# predict onset from personality
cox_onset_prev_laua <- coxph(Surv(onset_prev, event) ~ 
                          pers_o + pers_c + pers_e + pers_a + pers_n, 
                        data = df_uk_onset_prev_laua)
cox_onset_prev_laua %>% summary()

# predict onset from personality with controls
cox_onset_prev_laua_ctrl <- coxph(Surv(onset_prev, event) ~ 
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative,
                             data = df_uk_onset_prev_laua)
cox_onset_prev_laua_ctrl %>% summary()

```


## Predict prevalence slopes with linear models
```{r}

# predict slopes from personality
lm_slope_prev_laua <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_uk_slope_prev_laua)
lm_slope_prev_laua %>% summary()
lm_slope_prev_laua %>% confint(level=0.9)

# predict slopes from personality with controls
lm_slope_prev_laua_ctrl <- lm(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev,
                         data = df_uk_slope_prev_laua)
lm_slope_prev_laua_ctrl %>% summary()
lm_slope_prev_laua_ctrl %>% confint(level=0.9)

```

### CRF predicting slopes
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_slope_prev_laua <- cforest(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev,
                           data = df_uk_slope_prev, 
                         controls = ctrls)

crf_slope_prev_varimp <- varimp(crf_slope_prev_laua, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev_laua, conditional = T, nperm = 1)

crf_slope_prev_varimp
crf_slope_prev_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond
crf_slope_prev_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```


## Predict Social Distancing
### Change point analysis
```{r}

# keep only counties with full data
nuts_complete <- df_uk_socdist_scaled %>% 
  group_by(nuts3) %>% 
  summarize(n = n()) %>% 
  filter(n==max(.$n)) %>% 
  .$nuts3

# run changepoint analysis
df_uk_socdist_cpt_results <- df_uk_socdist_scaled %>% 
  select(nuts3, socdist_single_tile) %>%
  filter(nuts3 %in% nuts_complete) %>% 
  split(.$nuts3) %>%
  map(~ cpt.meanvar(as.vector(.$socdist_single_tile),
                    #penalty = 'Asymptotic',
                    class=TRUE,
                    param.estimates=TRUE,
                    Q=1,
                    test.stat = 'Normal'))

df_uk_socdist_cpt_results_2 <- df_uk_socdist_scaled %>% 
  select(nuts3, socdist_tiles) %>%
  filter(nuts3 %in% nuts_complete) %>% 
  split(.$nuts3) %>%
  map(~ cpt.meanvar(as.vector(.$socdist_tiles),
                    #penalty = 'Asymptotic',
                    class=TRUE,
                    param.estimates=TRUE,
                    Q=1,
                    test.stat = 'Normal'))

# calculate change point
df_uk_socdist_cpt_day <- df_uk_socdist_cpt_results %>% 
  map(cpts) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(cpt_day_socdist = '.') %>%
  rownames_to_column('nuts3')

df_uk_socdist_cpt_day_2 <- df_uk_socdist_cpt_results_2 %>% 
  map(cpts) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(cpt_day_socdist_2 = '.') %>%
  rownames_to_column('nuts3')

# calculate mean differences
df_uk_socdist_cpt_mean_diff <- df_uk_socdist_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$mean) %>% 
  map(~ .[2]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(mean_diff_socdist = '.') %>%
  rownames_to_column('nuts3')

df_uk_socdist_cpt_mean_diff_2 <- df_uk_socdist_cpt_results_2 %>% 
  map(param.est) %>% 
  map(~ .$mean) %>% 
  map(~ -.[2]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(mean_diff_socdist_2 = '.') %>%
  rownames_to_column('nuts3')

# calculate means 
df_uk_socdist_mean <- df_uk_socdist_scaled %>% 
  group_by(nuts3) %>%
  summarize(mean_socdist = mean(socdist_single_tile))

df_uk_socdist_mean_2 <- df_uk_socdist_scaled %>% 
  group_by(nuts3) %>%
  summarize(mean_socdist_2 = -mean(socdist_tiles))

# merge with county data
nuts_ut_key <- read_csv('nuts3_ut.csv')

df_uk_cpt_socdist <- df_uk_socdist_scaled %>% 
  select(-time, -socdist_single_tile, -socdist_tiles) %>%
  distinct() %>% 
  left_join(df_uk_socdist_cpt_day, by='nuts3') %>%
  left_join(df_uk_socdist_cpt_day_2, by='nuts3') %>%
  left_join(df_uk_socdist_cpt_mean_diff, by='nuts3') %>%
  left_join(df_uk_socdist_cpt_mean_diff_2, by='nuts3') %>%
  left_join(df_uk_socdist_mean, by='nuts3') %>%
  left_join(df_uk_socdist_mean_2, by='nuts3') %>%
  left_join(nuts_ut_key, by='nuts3') %>% 
  left_join(select(df_uk_onset_prev, ut_area, onset_prev), by='ut_area') %>%
  left_join(select(df_uk_slope_prev, ut_area, slope_prev), by='ut_area') %>%
  select(-ut_area)

# standardize mean/var differences
df_uk_cpt_socdist <- df_uk_cpt_socdist %>% 
  mutate(mean_diff_socdist = scale(mean_diff_socdist),
         mean_diff_socdist_2 = scale(mean_diff_socdist_2),
         mean_socdist = scale(mean_socdist),
         mean_socdist_2 = scale(mean_socdist_2))

# handle censored data
df_uk_cpt_socdist <- df_uk_cpt_socdist %>% 
  mutate(cpt_day_socdist = ifelse(is.na(cpt_day_socdist), 
                                  as.numeric(diff(range(df_uk_cpt_socdist$date))), 
                                  cpt_day_socdist)) %>% 
  mutate(event = ifelse(cpt_day_socdist >= 
                          as.numeric(diff(range(df_uk_socdist$date))), 0, 1))

```

```{r}
df_uk_cpt_socdist$cpt_day_socdist %>% hist()
df_uk_cpt_socdist$mean_diff_socdist %>% hist()
df_uk_cpt_socdist$mean_socdist %>% hist()


df_uk_cpt_socdist$cpt_day_socdist_2 %>% hist()
df_uk_cpt_socdist$mean_diff_socdist_2 %>% hist()
df_uk_cpt_socdist$mean_socdist_2 %>% hist()

```

```{r}

cor(df_uk_cpt_socdist$mean_diff_socdist, 
    df_uk_cpt_socdist$mean_socdist)

cor(df_uk_cpt_socdist$mean_diff_socdist_2, 
    df_uk_cpt_socdist$mean_socdist_2)

```

```{r}

for(i in head(df_uk_socdist_cpt_results, 5)){
  plot(i)
}

```
```{r}

for(i in head(df_uk_socdist_cpt_results_2, 5)){
  plot(i)
}

```

# Predicting change points with time-to-event regression 
```{r}

# predict hazard from personality
cox_cpt_socdist <- coxph(Surv(cpt_day_socdist, event) ~ 
                           pers_o + pers_c + pers_e + pers_a + pers_n, 
                  data = df_uk_cpt_socdist)
cox_cpt_socdist %>% summary()

# predict hazard from personality with controls
cox_cpt_socdist_ctrl <- coxph(Surv(cpt_day_socdist, event) ~ 
                                 pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev + slope_prev,
                  data = df_uk_cpt_socdist)
cox_cpt_socdist_ctrl %>% summary()

```

### Linear models predicting mean differences
```{r}

lm_meandiff_socdist <- lm(mean_diff_socdist ~ 
                            pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_uk_cpt_socdist)
lm_meandiff_socdist %>% summary()
lm_meandiff_socdist %>% confint(level=0.9)

lm_meandiff_socdist_ctrl <- lm(mean_diff_socdist ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev + slope_prev,
                            data = df_uk_cpt_socdist)
lm_meandiff_socdist_ctrl %>% summary()
lm_meandiff_socdist_ctrl %>% confint(level=0.9)

```

### CRF predicting mean difference
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_meandiff_socdist <- cforest(mean_diff_socdist ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev + slope_prev,
                               data = df_uk_cpt_socdist %>% drop_na(),
                         controls = ctrls)

crf_meandiff_socdist_varimp <- varimp(crf_meandiff_socdist, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist, conditional = T, nperm = 1)

crf_meandiff_socdist_varimp
crf_meandiff_socdist_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond
crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```

# Predicting change points with time-to-event regression 
```{r}

# predict hazard from personality
cox_cpt_socdist_2 <- coxph(Surv(cpt_day_socdist_2, event) ~ 
                           pers_o + pers_c + pers_e + pers_a + pers_n, 
                  data = df_uk_cpt_socdist)
cox_cpt_socdist_2 %>% summary()

# predict hazard from personality with controls
cox_cpt_socdist_ctrl_2 <- coxph(Surv(cpt_day_socdist_2, event) ~ 
                                 pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev + slope_prev,
                  data = df_uk_cpt_socdist)
cox_cpt_socdist_ctrl_2 %>% summary()

```

### Linear models predicting mean differences
```{r}

lm_meandiff_socdist_2 <- lm(mean_diff_socdist_2 ~ 
                            pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_uk_cpt_socdist)
lm_meandiff_socdist_2 %>% summary()
lm_meandiff_socdist_2 %>% confint(level=0.9)

lm_meandiff_socdist_ctrl_2 <- lm(mean_diff_socdist_2 ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev + slope_prev,
                            data = df_uk_cpt_socdist)
lm_meandiff_socdist_ctrl_2 %>% summary()
lm_meandiff_socdist_ctrl_2 %>% confint(level=0.9)

```

### CRF predicting mean difference
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_meandiff_socdist_2 <- cforest(mean_diff_socdist_2 ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               airport_dist + males + popdens + manufacturing + 
                               tourism + health + academic + medinc + medage +
                               conservative + onset_prev + slope_prev,
                               data = df_uk_cpt_socdist %>% drop_na(),
                         controls = ctrls)

crf_meandiff_socdist_varimp_ <- varimp(crf_meandiff_socdist_2, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist_2, conditional = T, nperm = 1)

crf_meandiff_socdist_varimp
crf_meandiff_socdist_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond
crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```


### Export data 
```{r}

uk_list_results <- list(cox_onset_prev_laua, cox_onset_prev_laua_ctrl, 
     lm_slope_prev_laua, lm_slope_prev_laua_ctrl, 
     cox_cpt_socdist, cox_cpt_socdist_ctrl,
     lm_meandiff_socdist, lm_meandiff_socdist_ctrl,
     cox_cpt_socdist_2, cox_cpt_socdist_ctrl_2,
     lm_meandiff_socdist_2, lm_meandiff_socdist_ctrl_2)

results_names <- list('cox_onset_prev', 'cox_onset_prev_ctrl', 
     'lm_slope_prev', 'lm_slope_prev_ctrl', 
     'cox_cpt_socdist', 'cox_cpt_socdist_ctrl', 
     'lm_meandiff_socdist', 'lm_meandiff_socdist_ctrl',
     'cox_cpt_socdist_2', 'cox_cpt_socdist_ctrl_2',
     'lm_meandiff_socdist_2', 'lm_meandiff_socdist_ctrl_2')

names(uk_list_results) <- results_names

save(uk_list_results, file="uk_list_results.RData")

```

```{r}

write_csv(df_uk_slope_prev, '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Delivery/df_uk_slope_prev.csv')
write_csv(df_uk_cpt_socdist, '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Delivery/df_uk_cpt_socdist.csv')

```
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